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UT Austin Selected as Home of National AI Institute Focused on Machine Learning

UT Austin Selected as Home of National AI Institute Focused on Machine Learning

The NSF AI Institute for Foundations of Machine Learning and the Machine Learning Laboratory will be administratively housed in the Gates-Dell Complex at The University of Texas at Austin. Photo credit: Vivian Abagiu/University of Texas at Austin.

The National Science Foundation has selected The University of Texas at Austin to lead the NSF AI Institute for Foundations of Machine Learning, bolstering the university's existing strengths in this emerging field. Machine learning is the technology that drives AI systems, enabling them to acquire knowledge and make predictions in complex environments. This technology has the potential to transform everything from transportation to entertainment to health care.

Students Help Build App to Aid UT Community As They Return to Campus

Students Help Build App to Aid UT Community As They Return to Campus

As students, faculty, and staff prepare to return to campus for the fall semester, a key concern is making the university as safe as possible and properly tracking health data to prevent outbreaks. An interdisciplinary team of researchers and students, including Texas Computer Science undergraduate students Rohit Neppali, Anshul Modh, Viren Velacheri, and Ph.D. student Anibal Heinsfeld, developed the Protect Texas Together app to help track and mitigate the spread of COVID-19 on the Forty Acres.

Computer Scientists Explore How Artificial Agents Collaborate on a Shared Task

Computer Scientists Explore How Artificial Agents Collaborate on a Shared Task

There's an (albeit cliché) saying that says that two heads are better than one. Unsurprisingly, this idiom extends to artificial agents. In the field of AI, researchers have been working to understand how to make independent agents, who may have different goals, work together in an environment to complete a shared task. Three researchers in the Department of Computer Science, graduate student Ishan Durugkar, recent doctoral alumnus Elad Liebman, and professor Peter Stone, have been working to solve this problem. 

UT Austin to Partner in New NSF Quantum Computing Institute

UT Austin to Partner in New NSF Quantum Computing Institute

Illustration credit: Nicolle R. Fuller/National Science Foundation

The University of Texas at Austin's Scott Aaronson is an initial member of a new multi-institution collaboration called the NSF Quantum Leap Challenge Institute for Present and Future Quantum Computation. The institute will work to overcome scientific challenges to achieving quantum computing and will design advanced, large-scale quantum computers that employ state-of-the-art scientific algorithms developed by the researchers.

Investigating How to Make Robots Better Team Members

Investigating How to Make Robots Better Team Members

Imagine that you are a robot in a hospital: composed of bolts and bits, running on code, and surrounded by humans. It's your first day on the job, and your task is to help your new human teammates—the hospital's employees—do their job more effectively and efficiently. Mainly, you're fetching things. You've never met the employees before, and don't know how they handle their tasks. How do you know when to ask for instructions? At what point does asking too many questions become disruptive?

Power of DNA to Store Information Gets an Upgrade

Power of DNA to Store Information Gets an Upgrade

A team of interdisciplinary researchers has discovered a new technique to store information in DNA – in this case "The Wizard of Oz," translated into Esperanto – with unprecedented accuracy and efficiency. The technique harnesses the information-storage capacity of intertwined strands of DNA to encode and retrieve information in a way that is both durable and compact. The technique is described in a paper in this week's Proceedings of the National Academy of Sciences.

Kristen Grauman Named Finalist in 2020 Blavatnik National Awards for Young Scientists

Kristen Grauman Named Finalist in 2020 Blavatnik National Awards for Young Scientists

University of Texas at Austin computer science researcher Kristen Grauman was selected as a finalist for the 2020 Blavatnik National Awards for Young Scientists.

Pangolin: An Efficient and Flexible Graph Pattern Mining System

Pangolin: An Efficient and Flexible Graph Pattern Mining System

The datasets used by many software applications can be represented as graphs, defined by sets of vertices and edges. These graphs are rich with useful information, and can be used to determine patterns and relationships among the stored data. This process of discovering relevant patterns from graphs is called Graph Pattern Mining. A team of Texas Computer Science (TXCS) researchers advised by Dr. Keshav Pingali has done groundbreaking work to make these programs more efficient and accessible. Their work was recently accepted to Very Large Databases (VLDB) 2020, one of the premier conferences in computer science.

New Online Master’s Degree in Data Science is a First for The University of Texas at Austin

New Online Master’s Degree in Data Science is a First for The University of Texas at Austin

In response to high demand for professionals with scientific and technical training to understand and work with massive amounts of data, The University of Texas at Austin is set to launch a new online master's degree program in data science. Pending final approval by UT System and the Texas Higher Education Coordinating Board, the new program will be a collaboration between the Department of Computer Science, ranked among the top 10 programs in the country by U.S. News and World Report; the Department of Statistics and Data Sciences, one of the university's newest and fastest-growing departments; and online learning company edX.

Researchers Design Evolutionary Algorithms for Neural Networks

Researchers Design Evolutionary Algorithms for Neural Networks

Texas Computer Science graduate students Garrett Bingham and William Macke, under the advisement of professor Risto Miikkulainen, are contributing to the improvement of AI with their research. Their paper, entitled "Evolutionary Optimization of Deep Learning Activation Functions" was accepted into the 2020 Genetic and Evolutionary Computation Conference. The work concerns the evolutionary optimization of activation functions as a potential means of improving neural networks, which may ultimately lead to the creation of smarter and more accurate AI.